With the development of radio and space technologies, several satellite-based navigation systems (i.e. satellite positioning system or “SPS”) have already been built and more will be in use in the near future. SPS receivers, such as, for example, receivers using the Global Positioning System (“GPS”), also known as NAVSTAR, have become commonplace. Other examples of SPS systems include but are not limited to the United States (“U.S.”) Navy Navigation Satellite System (“NNSS”) (also known as TRANSIT), the Russian counterpart to NAVSTAR known as the Global Navigation Satellite System (“GLONASS”) and any future Western European SPS such as the proposed “Galileo” program. As an example, the U.S. NAVSTAR GPS system is described in GPS Theory and Practice, Fifth ed., revised edition by Hofmann-Wellenhof, Lichtenegger and Collins, Springer-Verlag Wien New York, 2001, which is fully incorporated herein by reference.
The U.S. GPS system was built and is operated by the United States Department of Defense. The system uses twenty-four or more satellites orbiting the earth at an altitude of about 11,000 miles with a period of about twelve hours. These satellites are placed in six different orbits such that at any time a minimum of six satellites are visible at any location on the surface of the earth except in the polar region. Each satellite transmits a time and position signal referenced to an atomic clock. A typical GPS receiver locks onto this signal and extracts the data contained in it. Using signals from a sufficient number of satellites, a GPS receiver can calculate its position, velocity, altitude, and time (i.e. navigation solution).
GPS and other satellite based navigational systems have some limitations such as the availability of a sufficient number of satellite signals. Satellite signals are sometimes not available in deep canyons, in areas with large numbers of buildings blocking the direct satellite signals, and in dense forest areas, for example. In addition, the satellite signals can be completely blocked or greatly attenuated inside buildings. Further, tunnels and bridges can block satellite signals resulting in large jumps in the indicated position at the exit of the tunnel after new satellite signals are received.
To address these issues, other complementary methods are sometimes used with satellite navigational systems to prevent interruptions in the position information. For example, sensors such as compasses can be used to determine heading, while speedometers can detect velocity. Meanwhile, inertial measurement units (IMUs) such as gyroscopes can be used to measure changes in heading or direction. Other types of IMUs such as accelerometers are used to estimate the acceleration of the navigation system, both backwards and forwards and from side to side. A host of similar devices can be used to improve the accuracy and the consistency of a navigation system such as those using GPS.
Accordingly, after the position of a system is initially determined, the sensors and/or IMUs allow the position to be determined as the system moves, even if the satellite signals are blocked. The determination of the position based on measuring the system's movement is known as dead reckoning (i.e. inertial navigation). The accuracy of a dead reckoning position and how long it remains accurate depends on various factors such as the quality of the sensors and how well they are calibrated. In some systems dead reckoning is also used to improve the accuracy of the satellite location determinations.
One type of device that can be used to assist in inertial navigation and/or dead reckoning is a magnetometer. Good calibration of such a magnetic sensor is very important for proper utilization of measured earth magnetic field data to accurately derive the heading angle. Accurately computing heading angle from magnetic sensor data is affected by factors such as the soft iron effect, hard iron effect, magnetic anomalies and other disturbances from surrounding environment.
Existing calibration processes are typically manually performed and require that the magnetic sensor is rotated by 360 degrees in two perpendicular planes while the measurement data is collected. Techniques such as the Minimum Volume Enclosing Ellipse (MVEE) method are then used for calibrating hard-iron effect and scale factor errors. The Extended Kalman filter is also used but the performance is limited by the accuracy of local inclination of magnetic field and the initial heading. Moreover, a method of continuous automated calibration of magnetic sensor has not been addressed in the prior art.
A need exists, therefore, for continuous calibration of a magnetic sensor that will have minimal requirement for the user action to collect measurement data to be used for calibration.